Overview

Dataset statistics

 TrainTest
Number of variables2220
Number of observations81012026
Missing cells00
Missing cells (%)0.0%0.0%
Duplicate rows00
Duplicate rows (%)0.0%0.0%
Total size in memory1.4 MiB316.7 KiB
Average record size in memory176.0 B160.1 B

Variable types

 TrainTest
Numeric1615
Categorical65

Alerts

TrainTest
Customer_Age is highly overall correlated with Months_on_bookCustomer_Age is highly overall correlated with Months_on_bookHigh Correlation
Months_on_book is highly overall correlated with Customer_AgeMonths_on_book is highly overall correlated with Customer_AgeHigh Correlation
Credit_Limit is highly overall correlated with Avg_Open_To_BuyCredit_Limit is highly overall correlated with Avg_Open_To_BuyHigh Correlation
Total_Revolving_Bal is highly overall correlated with Avg_Utilization_RatioTotal_Revolving_Bal is highly overall correlated with Avg_Utilization_RatioHigh Correlation
Avg_Open_To_Buy is highly overall correlated with Credit_Limit and 1 other fieldsAvg_Open_To_Buy is highly overall correlated with Credit_Limit and 1 other fieldsHigh Correlation
Total_Trans_Amt is highly overall correlated with Total_Trans_CtTotal_Trans_Amt is highly overall correlated with Total_Trans_CtHigh Correlation
Total_Trans_Ct is highly overall correlated with Total_Trans_AmtTotal_Trans_Ct is highly overall correlated with Total_Trans_AmtHigh Correlation
Avg_Utilization_Ratio is highly overall correlated with Total_Revolving_Bal and 1 other fieldsAvg_Utilization_Ratio is highly overall correlated with Total_Revolving_Bal and 1 other fieldsHigh Correlation
Gender is highly overall correlated with Income_CategoryGender is highly overall correlated with Income_CategoryHigh Correlation
Income_Category is highly overall correlated with GenderIncome_Category is highly overall correlated with GenderHigh Correlation
Card_Category is highly imbalanced (79.4%) Card_Category is highly imbalanced (78.2%) Imbalance
train_idx is uniformly distributed Alert not present in Uniform
train_idx has unique values Alert not present in Unique
CLIENTNUM has unique values CLIENTNUM has unique values Unique
Dependent_count has 725 (8.9%) zeros Dependent_count has 179 (8.8%) zeros Zeros
Contacts_Count_12_mon has 312 (3.9%) zeros Contacts_Count_12_mon has 87 (4.3%) zeros Zeros
Total_Revolving_Bal has 1986 (24.5%) zeros Total_Revolving_Bal has 484 (23.9%) zeros Zeros
Avg_Utilization_Ratio has 1986 (24.5%) zeros Avg_Utilization_Ratio has 484 (23.9%) zeros Zeros

Reproduction

 TrainTest
Analysis started2023-04-12 09:58:25.3158302023-04-12 09:59:06.356486
Analysis finished2023-04-12 09:59:06.3455212023-04-12 09:59:43.584603
Duration41.03 seconds37.23 seconds
Software versionydata-profiling vv4.1.2ydata-profiling vv4.1.2
Download configurationconfig.jsonconfig.json

Variables

train_idx
Real number (ℝ)

Distinct8101
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4050
Minimum0
Maximum8100
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2023-04-12T11:59:43.709174image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile405
Q12025
median4050
Q36075
95-th percentile7695
Maximum8100
Range8100
Interquartile range (IQR)4050

Descriptive statistics

Standard deviation2338.7016
Coefficient of variation (CV)0.57745718
Kurtosis-1.2
Mean4050
Median Absolute Deviation (MAD)2025
Skewness0
Sum32809050
Variance5469525.2
MonotonicityStrictly increasing
2023-04-12T11:59:43.903422image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
5396 1
 
< 0.1%
5409 1
 
< 0.1%
5408 1
 
< 0.1%
5407 1
 
< 0.1%
5406 1
 
< 0.1%
5405 1
 
< 0.1%
5404 1
 
< 0.1%
5403 1
 
< 0.1%
5402 1
 
< 0.1%
Other values (8091) 8091
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
8100 1
< 0.1%
8099 1
< 0.1%
8098 1
< 0.1%
8097 1
< 0.1%
8096 1
< 0.1%
8095 1
< 0.1%
8094 1
< 0.1%
8093 1
< 0.1%
8092 1
< 0.1%
8091 1
< 0.1%

CLIENTNUM
Real number (ℝ)

 TrainTest
Distinct81012026
Distinct (%)100.0%100.0%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean7.3913295 × 1087.3935618 × 108
 TrainTest
Minimum7.0808208 × 1087.0809513 × 108
Maximum8.2834308 × 1088.2829186 × 108
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size63.4 KiB16.0 KiB
2023-04-12T11:59:44.149128image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum7.0808208 × 1087.0809513 × 108
5-th percentile7.0910126 × 1087.0919846 × 108
Q17.1305338 × 1087.1298125 × 108
median7.1788601 × 1087.180845 × 108
Q37.7284638 × 1087.750037 × 108
95-th percentile8.1397128 × 1088.1460635 × 108
Maximum8.2834308 × 1088.2829186 × 108
Range1.20261 × 1081.2019672 × 108
Interquartile range (IQR)5979300062022450

Descriptive statistics

 TrainTest
Standard deviation3691911636850975
Coefficient of variation (CV)0.0499492220.049841979
Kurtosis-0.60777433-0.64543035
Mean7.3913295 × 1087.3935618 × 108
Median Absolute Deviation (MAD)62922006597337.5
Skewness1.00053220.97658867
Sum5.987716 × 10121.4979356 × 1012
Variance1.3630211 × 10151.3579944 × 1015
MonotonicityNot monotonicNot monotonic
2023-04-12T11:59:44.414518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
713071383 1
 
< 0.1%
759947433 1
 
< 0.1%
709841433 1
 
< 0.1%
789483333 1
 
< 0.1%
785328408 1
 
< 0.1%
805534608 1
 
< 0.1%
709005633 1
 
< 0.1%
718246458 1
 
< 0.1%
789917208 1
 
< 0.1%
778377183 1
 
< 0.1%
Other values (8091) 8091
99.9%
ValueCountFrequency (%)
719455083 1
 
< 0.1%
720539583 1
 
< 0.1%
721445658 1
 
< 0.1%
713956308 1
 
< 0.1%
716385033 1
 
< 0.1%
779642733 1
 
< 0.1%
712815858 1
 
< 0.1%
716543433 1
 
< 0.1%
714121983 1
 
< 0.1%
708254733 1
 
< 0.1%
Other values (2016) 2016
99.5%
ValueCountFrequency (%)
708082083 1
< 0.1%
708083283 1
< 0.1%
708084558 1
< 0.1%
708085458 1
< 0.1%
708086958 1
< 0.1%
708098133 1
< 0.1%
708099183 1
< 0.1%
708100533 1
< 0.1%
708103608 1
< 0.1%
708104658 1
< 0.1%
ValueCountFrequency (%)
708095133 1
< 0.1%
708112008 1
< 0.1%
708152358 1
< 0.1%
708160008 1
< 0.1%
708170508 1
< 0.1%
708173433 1
< 0.1%
708185208 1
< 0.1%
708211383 1
< 0.1%
708219858 1
< 0.1%
708222558 1
< 0.1%
ValueCountFrequency (%)
708095133 1
< 0.1%
708112008 1
< 0.1%
708152358 1
< 0.1%
708160008 1
< 0.1%
708170508 1
< 0.1%
708173433 1
< 0.1%
708185208 1
< 0.1%
708211383 1
< 0.1%
708219858 1
< 0.1%
708222558 1
< 0.1%
ValueCountFrequency (%)
708082083 1
< 0.1%
708083283 1
< 0.1%
708084558 1
< 0.1%
708085458 1
< 0.1%
708086958 1
< 0.1%
708098133 1
< 0.1%
708099183 1
< 0.1%
708100533 1
< 0.1%
708103608 1
< 0.1%
708104658 1
< 0.1%

Customer_Age
Real number (ℝ)

 TrainTest
Distinct4443
Distinct (%)0.5%2.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean46.30638246.404245
 TrainTest
Minimum2626
Maximum7073
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size63.4 KiB16.0 KiB
2023-04-12T11:59:44.656098image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum2626
5-th percentile3333
Q14141
median4646
Q35252
95-th percentile6060
Maximum7073
Range4447
Interquartile range (IQR)1111

Descriptive statistics

 TrainTest
Standard deviation8.02252667.9954284
Coefficient of variation (CV)0.173248830.17229951
Kurtosis-0.31128146-0.19649916
Mean46.30638246.404245
Median Absolute Deviation (MAD)65
Skewness-0.0438125360.0078513946
Sum37512894015
Variance64.36093363.926876
MonotonicityNot monotonicNot monotonic
2023-04-12T11:59:44.886172image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
46 397
 
4.9%
49 395
 
4.9%
45 390
 
4.8%
44 386
 
4.8%
47 378
 
4.7%
48 372
 
4.6%
43 371
 
4.6%
50 367
 
4.5%
42 326
 
4.0%
51 325
 
4.0%
Other values (34) 4394
54.2%
ValueCountFrequency (%)
44 114
 
5.6%
43 102
 
5.0%
47 101
 
5.0%
48 100
 
4.9%
49 100
 
4.9%
42 100
 
4.9%
45 96
 
4.7%
46 93
 
4.6%
53 86
 
4.2%
50 85
 
4.2%
Other values (33) 1049
51.8%
ValueCountFrequency (%)
26 63
0.8%
27 22
 
0.3%
28 24
 
0.3%
29 50
 
0.6%
30 53
 
0.7%
31 78
1.0%
32 90
1.1%
33 98
1.2%
34 126
1.6%
35 143
1.8%
ValueCountFrequency (%)
26 15
 
0.7%
27 10
 
0.5%
28 5
 
0.2%
29 6
 
0.3%
30 17
0.8%
31 13
 
0.6%
32 16
 
0.8%
33 29
1.4%
34 20
1.0%
35 41
2.0%
ValueCountFrequency (%)
26 15
 
0.2%
27 10
 
0.1%
28 5
 
0.1%
29 6
 
0.1%
30 17
0.2%
31 13
 
0.2%
32 16
 
0.2%
33 29
0.4%
34 20
0.2%
35 41
0.5%
ValueCountFrequency (%)
26 63
3.1%
27 22
 
1.1%
28 24
 
1.2%
29 50
 
2.5%
30 53
 
2.6%
31 78
3.8%
32 90
4.4%
33 98
4.8%
34 126
6.2%
35 143
7.1%

Gender
Categorical

 TrainTest
Distinct22
Distinct (%)< 0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Memory size63.4 KiB16.0 KiB
F
4279 
M
3822 
F
1079 
M
947 

Length

 TrainTest
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 TrainTest
Total characters81012026
Distinct characters22
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 TrainTest
Unique00 ?
Unique (%)0.0%0.0%

Sample

 TrainTest
1st rowFF
2nd rowFM
3rd rowFF
4th rowFM
5th rowFM

Common Values

ValueCountFrequency (%)
F 4279
52.8%
M 3822
47.2%
ValueCountFrequency (%)
F 1079
53.3%
M 947
46.7%

Length

2023-04-12T11:59:45.054711image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Train

2023-04-12T11:59:45.197884image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:45.341427image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
f 4279
52.8%
m 3822
47.2%
ValueCountFrequency (%)
f 1079
53.3%
m 947
46.7%

Most occurring characters

ValueCountFrequency (%)
F 4279
52.8%
M 3822
47.2%
ValueCountFrequency (%)
F 1079
53.3%
M 947
46.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 8101
100.0%
ValueCountFrequency (%)
Uppercase Letter 2026
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 4279
52.8%
M 3822
47.2%
ValueCountFrequency (%)
F 1079
53.3%
M 947
46.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 8101
100.0%
ValueCountFrequency (%)
Latin 2026
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 4279
52.8%
M 3822
47.2%
ValueCountFrequency (%)
F 1079
53.3%
M 947
46.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8101
100.0%
ValueCountFrequency (%)
ASCII 2026
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 4279
52.8%
M 3822
47.2%
ValueCountFrequency (%)
F 1079
53.3%
M 947
46.7%

Dependent_count
Real number (ℝ)

 TrainTest
Distinct66
Distinct (%)0.1%0.3%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean2.33477352.3919052
 TrainTest
Minimum00
Maximum55
Zeros725179
Zeros (%)8.9%8.8%
Negative00
Negative (%)0.0%0.0%
Memory size63.4 KiB16.0 KiB
2023-04-12T11:59:45.454853image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q111
median22
Q333
95-th percentile44
Maximum55
Range55
Interquartile range (IQR)22

Descriptive statistics

 TrainTest
Standard deviation1.28956371.334967
Coefficient of variation (CV)0.552329270.55811869
Kurtosis-0.65676736-0.78223964
Mean2.33477352.3919052
Median Absolute Deviation (MAD)11
Skewness-0.020167501-0.030107582
Sum189144846
Variance1.66297461.7821369
MonotonicityNot monotonicNot monotonic
2023-04-12T11:59:45.585608image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 2222
27.4%
2 2150
26.5%
1 1465
18.1%
4 1212
15.0%
0 725
 
8.9%
5 327
 
4.0%
ValueCountFrequency (%)
3 510
25.2%
2 505
24.9%
1 373
18.4%
4 362
17.9%
0 179
 
8.8%
5 97
 
4.8%
ValueCountFrequency (%)
0 725
 
8.9%
1 1465
18.1%
2 2150
26.5%
3 2222
27.4%
4 1212
15.0%
5 327
 
4.0%
ValueCountFrequency (%)
0 179
 
8.8%
1 373
18.4%
2 505
24.9%
3 510
25.2%
4 362
17.9%
5 97
 
4.8%
ValueCountFrequency (%)
0 179
 
2.2%
1 373
4.6%
2 505
6.2%
3 510
6.3%
4 362
4.5%
5 97
 
1.2%
ValueCountFrequency (%)
0 725
 
35.8%
1 1465
72.3%
2 2150
106.1%
3 2222
109.7%
4 1212
59.8%
5 327
 
16.1%

Education_Level
Categorical

 TrainTest
Distinct77
Distinct (%)0.1%0.3%
Missing00
Missing (%)0.0%0.0%
Memory size63.4 KiB16.0 KiB
Graduate
2528 
High School
1619 
Unknown
1205 
Uneducated
1171 
College
816 
Other values (2)
762 
Graduate
600 
High School
394 
Uneducated
316 
Unknown
314 
College
197 
Other values (2)
205 

Length

 TrainTest
Max length1313
Median length1111
Mean length8.93420578.9595262
Min length77

Characters and Unicode

 TrainTest
Total characters7237618152
Distinct characters2525
Distinct categories44 ?
Distinct scripts22 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 TrainTest
Unique00 ?
Unique (%)0.0%0.0%

Sample

 TrainTest
1st rowUnknownUneducated
2nd rowHigh SchoolUneducated
3rd rowUnknownGraduate
4th rowGraduateDoctorate
5th rowHigh SchoolUnknown

Common Values

ValueCountFrequency (%)
Graduate 2528
31.2%
High School 1619
20.0%
Unknown 1205
14.9%
Uneducated 1171
14.5%
College 816
 
10.1%
Post-Graduate 407
 
5.0%
Doctorate 355
 
4.4%
ValueCountFrequency (%)
Graduate 600
29.6%
High School 394
19.4%
Uneducated 316
15.6%
Unknown 314
15.5%
College 197
 
9.7%
Post-Graduate 109
 
5.4%
Doctorate 96
 
4.7%

Length

2023-04-12T11:59:45.735266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Train

2023-04-12T11:59:45.923476image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:46.126536image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
graduate 2528
26.0%
high 1619
16.7%
school 1619
16.7%
unknown 1205
12.4%
uneducated 1171
12.0%
college 816
 
8.4%
post-graduate 407
 
4.2%
doctorate 355
 
3.7%
ValueCountFrequency (%)
graduate 600
24.8%
high 394
16.3%
school 394
16.3%
uneducated 316
13.1%
unknown 314
13.0%
college 197
 
8.1%
post-graduate 109
 
4.5%
doctorate 96
 
4.0%

Most occurring characters

ValueCountFrequency (%)
a 7396
 
10.2%
e 7264
 
10.0%
o 6376
 
8.8%
d 5277
 
7.3%
t 5223
 
7.2%
n 4786
 
6.6%
u 4106
 
5.7%
r 3290
 
4.5%
l 3251
 
4.5%
h 3238
 
4.5%
Other values (15) 22169
30.6%
ValueCountFrequency (%)
e 1831
 
10.1%
a 1830
 
10.1%
o 1600
 
8.8%
d 1341
 
7.4%
t 1326
 
7.3%
n 1258
 
6.9%
u 1025
 
5.6%
c 806
 
4.4%
r 805
 
4.4%
h 788
 
4.3%
Other values (15) 5542
30.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 60223
83.2%
Uppercase Letter 10127
 
14.0%
Space Separator 1619
 
2.2%
Dash Punctuation 407
 
0.6%
ValueCountFrequency (%)
Lowercase Letter 15120
83.3%
Uppercase Letter 2529
 
13.9%
Space Separator 394
 
2.2%
Dash Punctuation 109
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 7396
12.3%
e 7264
12.1%
o 6376
10.6%
d 5277
8.8%
t 5223
8.7%
n 4786
7.9%
u 4106
6.8%
r 3290
 
5.5%
l 3251
 
5.4%
h 3238
 
5.4%
Other values (6) 10016
16.6%
ValueCountFrequency (%)
e 1831
12.1%
a 1830
12.1%
o 1600
10.6%
d 1341
8.9%
t 1326
8.8%
n 1258
8.3%
u 1025
6.8%
c 806
 
5.3%
r 805
 
5.3%
h 788
 
5.2%
Other values (6) 2510
16.6%
Uppercase Letter
ValueCountFrequency (%)
G 2935
29.0%
U 2376
23.5%
S 1619
16.0%
H 1619
16.0%
C 816
 
8.1%
P 407
 
4.0%
D 355
 
3.5%
ValueCountFrequency (%)
G 709
28.0%
U 630
24.9%
S 394
15.6%
H 394
15.6%
C 197
 
7.8%
P 109
 
4.3%
D 96
 
3.8%
Space Separator
ValueCountFrequency (%)
1619
100.0%
ValueCountFrequency (%)
394
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 407
100.0%
ValueCountFrequency (%)
- 109
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 70350
97.2%
Common 2026
 
2.8%
ValueCountFrequency (%)
Latin 17649
97.2%
Common 503
 
2.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 7396
 
10.5%
e 7264
 
10.3%
o 6376
 
9.1%
d 5277
 
7.5%
t 5223
 
7.4%
n 4786
 
6.8%
u 4106
 
5.8%
r 3290
 
4.7%
l 3251
 
4.6%
h 3238
 
4.6%
Other values (13) 20143
28.6%
ValueCountFrequency (%)
e 1831
 
10.4%
a 1830
 
10.4%
o 1600
 
9.1%
d 1341
 
7.6%
t 1326
 
7.5%
n 1258
 
7.1%
u 1025
 
5.8%
c 806
 
4.6%
r 805
 
4.6%
h 788
 
4.5%
Other values (13) 5039
28.6%
Common
ValueCountFrequency (%)
1619
79.9%
- 407
 
20.1%
ValueCountFrequency (%)
394
78.3%
- 109
 
21.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72376
100.0%
ValueCountFrequency (%)
ASCII 18152
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 7396
 
10.2%
e 7264
 
10.0%
o 6376
 
8.8%
d 5277
 
7.3%
t 5223
 
7.2%
n 4786
 
6.6%
u 4106
 
5.7%
r 3290
 
4.5%
l 3251
 
4.5%
h 3238
 
4.5%
Other values (15) 22169
30.6%
ValueCountFrequency (%)
e 1831
 
10.1%
a 1830
 
10.1%
o 1600
 
8.8%
d 1341
 
7.4%
t 1326
 
7.3%
n 1258
 
6.9%
u 1025
 
5.6%
c 806
 
4.4%
r 805
 
4.4%
h 788
 
4.3%
Other values (15) 5542
30.5%

Marital_Status
Categorical

 TrainTest
Distinct44
Distinct (%)< 0.1%0.2%
Missing00
Missing (%)0.0%0.0%
Memory size63.4 KiB16.0 KiB
Married
3767 
Single
3144 
Divorced
611 
Unknown
579 
Married
920 
Single
799 
Unknown
170 
Divorced
137 

Length

 TrainTest
Max length88
Median length77
Mean length6.68732266.6732478
Min length66

Characters and Unicode

 TrainTest
Total characters5417413520
Distinct characters1717
Distinct categories22 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 TrainTest
Unique00 ?
Unique (%)0.0%0.0%

Sample

 TrainTest
1st rowSingleSingle
2nd rowMarriedSingle
3rd rowSingleDivorced
4th rowSingleDivorced
5th rowMarriedSingle

Common Values

ValueCountFrequency (%)
Married 3767
46.5%
Single 3144
38.8%
Divorced 611
 
7.5%
Unknown 579
 
7.1%
ValueCountFrequency (%)
Married 920
45.4%
Single 799
39.4%
Unknown 170
 
8.4%
Divorced 137
 
6.8%

Length

2023-04-12T11:59:46.303593image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Train

2023-04-12T11:59:46.477651image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:46.637185image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
married 3767
46.5%
single 3144
38.8%
divorced 611
 
7.5%
unknown 579
 
7.1%
ValueCountFrequency (%)
married 920
45.4%
single 799
39.4%
unknown 170
 
8.4%
divorced 137
 
6.8%

Most occurring characters

ValueCountFrequency (%)
r 8145
15.0%
i 7522
13.9%
e 7522
13.9%
n 4881
9.0%
d 4378
8.1%
M 3767
7.0%
a 3767
7.0%
l 3144
 
5.8%
g 3144
 
5.8%
S 3144
 
5.8%
Other values (7) 4760
8.8%
ValueCountFrequency (%)
r 1977
14.6%
i 1856
13.7%
e 1856
13.7%
n 1309
9.7%
d 1057
7.8%
M 920
6.8%
a 920
6.8%
l 799
5.9%
g 799
5.9%
S 799
5.9%
Other values (7) 1228
9.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 46073
85.0%
Uppercase Letter 8101
 
15.0%
ValueCountFrequency (%)
Lowercase Letter 11494
85.0%
Uppercase Letter 2026
 
15.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 8145
17.7%
i 7522
16.3%
e 7522
16.3%
n 4881
10.6%
d 4378
9.5%
a 3767
8.2%
l 3144
 
6.8%
g 3144
 
6.8%
o 1190
 
2.6%
v 611
 
1.3%
Other values (3) 1769
 
3.8%
ValueCountFrequency (%)
r 1977
17.2%
i 1856
16.1%
e 1856
16.1%
n 1309
11.4%
d 1057
9.2%
a 920
8.0%
l 799
7.0%
g 799
7.0%
o 307
 
2.7%
k 170
 
1.5%
Other values (3) 444
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
M 3767
46.5%
S 3144
38.8%
D 611
 
7.5%
U 579
 
7.1%
ValueCountFrequency (%)
M 920
45.4%
S 799
39.4%
U 170
 
8.4%
D 137
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 54174
100.0%
ValueCountFrequency (%)
Latin 13520
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 8145
15.0%
i 7522
13.9%
e 7522
13.9%
n 4881
9.0%
d 4378
8.1%
M 3767
7.0%
a 3767
7.0%
l 3144
 
5.8%
g 3144
 
5.8%
S 3144
 
5.8%
Other values (7) 4760
8.8%
ValueCountFrequency (%)
r 1977
14.6%
i 1856
13.7%
e 1856
13.7%
n 1309
9.7%
d 1057
7.8%
M 920
6.8%
a 920
6.8%
l 799
5.9%
g 799
5.9%
S 799
5.9%
Other values (7) 1228
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54174
100.0%
ValueCountFrequency (%)
ASCII 13520
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 8145
15.0%
i 7522
13.9%
e 7522
13.9%
n 4881
9.0%
d 4378
8.1%
M 3767
7.0%
a 3767
7.0%
l 3144
 
5.8%
g 3144
 
5.8%
S 3144
 
5.8%
Other values (7) 4760
8.8%
ValueCountFrequency (%)
r 1977
14.6%
i 1856
13.7%
e 1856
13.7%
n 1309
9.7%
d 1057
7.8%
M 920
6.8%
a 920
6.8%
l 799
5.9%
g 799
5.9%
S 799
5.9%
Other values (7) 1228
9.1%

Income_Category
Categorical

 TrainTest
Distinct66
Distinct (%)0.1%0.3%
Missing00
Missing (%)0.0%0.0%
Memory size63.4 KiB16.0 KiB
Less than $40K
2812 
$40K - $60K
1453 
$80K - $120K
1237 
$60K - $80K
1122 
Unknown
889 
Less than $40K
749 
$40K - $60K
337 
$80K - $120K
298 
$60K - $80K
280 
Unknown
223 

Length

 TrainTest
Max length1414
Median length1212
Mean length11.46475711.541461
Min length77

Characters and Unicode

 TrainTest
Total characters9287623383
Distinct characters2222
Distinct categories77 ?
Distinct scripts22 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 TrainTest
Unique00 ?
Unique (%)0.0%0.0%

Sample

 TrainTest
1st rowUnknownLess than $40K
2nd rowUnknownLess than $40K
3rd rowLess than $40KLess than $40K
4th rowLess than $40K$40K - $60K
5th row$40K - $60K$80K - $120K

Common Values

ValueCountFrequency (%)
Less than $40K 2812
34.7%
$40K - $60K 1453
17.9%
$80K - $120K 1237
15.3%
$60K - $80K 1122
 
13.9%
Unknown 889
 
11.0%
$120K + 588
 
7.3%
ValueCountFrequency (%)
Less than $40K 749
37.0%
$40K - $60K 337
16.6%
$80K - $120K 298
 
14.7%
$60K - $80K 280
 
13.8%
Unknown 223
 
11.0%
$120K + 139
 
6.9%

Length

2023-04-12T11:59:46.784936image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Train

2023-04-12T11:59:46.976125image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:47.190184image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
4400
20.1%
40k 4265
19.4%
less 2812
12.8%
than 2812
12.8%
60k 2575
11.7%
80k 2359
10.8%
120k 1825
8.3%
unknown 889
 
4.1%
ValueCountFrequency (%)
40k 1086
19.8%
1054
19.2%
less 749
13.6%
than 749
13.6%
60k 617
11.2%
80k 578
10.5%
120k 437
8.0%
unknown 223
 
4.1%

Most occurring characters

ValueCountFrequency (%)
13836
14.9%
K 11024
11.9%
0 11024
11.9%
$ 11024
11.9%
s 5624
 
6.1%
n 5479
 
5.9%
4 4265
 
4.6%
- 3812
 
4.1%
e 2812
 
3.0%
L 2812
 
3.0%
Other values (12) 21164
22.8%
ValueCountFrequency (%)
3467
14.8%
K 2718
11.6%
0 2718
11.6%
$ 2718
11.6%
s 1498
 
6.4%
n 1418
 
6.1%
4 1086
 
4.6%
- 915
 
3.9%
e 749
 
3.2%
L 749
 
3.2%
Other values (12) 5347
22.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 25018
26.9%
Decimal Number 23873
25.7%
Uppercase Letter 14725
15.9%
Space Separator 13836
14.9%
Currency Symbol 11024
11.9%
Dash Punctuation 3812
 
4.1%
Math Symbol 588
 
0.6%
ValueCountFrequency (%)
Lowercase Letter 6581
28.1%
Decimal Number 5873
25.1%
Uppercase Letter 3690
15.8%
Space Separator 3467
14.8%
Currency Symbol 2718
11.6%
Dash Punctuation 915
 
3.9%
Math Symbol 139
 
0.6%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
13836
100.0%
ValueCountFrequency (%)
3467
100.0%
Uppercase Letter
ValueCountFrequency (%)
K 11024
74.9%
L 2812
 
19.1%
U 889
 
6.0%
ValueCountFrequency (%)
K 2718
73.7%
L 749
 
20.3%
U 223
 
6.0%
Decimal Number
ValueCountFrequency (%)
0 11024
46.2%
4 4265
 
17.9%
6 2575
 
10.8%
8 2359
 
9.9%
1 1825
 
7.6%
2 1825
 
7.6%
ValueCountFrequency (%)
0 2718
46.3%
4 1086
 
18.5%
6 617
 
10.5%
8 578
 
9.8%
1 437
 
7.4%
2 437
 
7.4%
Currency Symbol
ValueCountFrequency (%)
$ 11024
100.0%
ValueCountFrequency (%)
$ 2718
100.0%
Lowercase Letter
ValueCountFrequency (%)
s 5624
22.5%
n 5479
21.9%
e 2812
11.2%
a 2812
11.2%
h 2812
11.2%
t 2812
11.2%
k 889
 
3.6%
o 889
 
3.6%
w 889
 
3.6%
ValueCountFrequency (%)
s 1498
22.8%
n 1418
21.5%
e 749
11.4%
a 749
11.4%
h 749
11.4%
t 749
11.4%
k 223
 
3.4%
o 223
 
3.4%
w 223
 
3.4%
Dash Punctuation
ValueCountFrequency (%)
- 3812
100.0%
ValueCountFrequency (%)
- 915
100.0%
Math Symbol
ValueCountFrequency (%)
+ 588
100.0%
ValueCountFrequency (%)
+ 139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 53133
57.2%
Latin 39743
42.8%
ValueCountFrequency (%)
Common 13112
56.1%
Latin 10271
43.9%

Most frequent character per script

Common
ValueCountFrequency (%)
13836
26.0%
0 11024
20.7%
$ 11024
20.7%
4 4265
 
8.0%
- 3812
 
7.2%
6 2575
 
4.8%
8 2359
 
4.4%
1 1825
 
3.4%
2 1825
 
3.4%
+ 588
 
1.1%
ValueCountFrequency (%)
3467
26.4%
0 2718
20.7%
$ 2718
20.7%
4 1086
 
8.3%
- 915
 
7.0%
6 617
 
4.7%
8 578
 
4.4%
1 437
 
3.3%
2 437
 
3.3%
+ 139
 
1.1%
Latin
ValueCountFrequency (%)
K 11024
27.7%
s 5624
14.2%
n 5479
13.8%
e 2812
 
7.1%
L 2812
 
7.1%
a 2812
 
7.1%
h 2812
 
7.1%
t 2812
 
7.1%
U 889
 
2.2%
k 889
 
2.2%
Other values (2) 1778
 
4.5%
ValueCountFrequency (%)
K 2718
26.5%
s 1498
14.6%
n 1418
13.8%
e 749
 
7.3%
L 749
 
7.3%
a 749
 
7.3%
h 749
 
7.3%
t 749
 
7.3%
U 223
 
2.2%
k 223
 
2.2%
Other values (2) 446
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 92876
100.0%
ValueCountFrequency (%)
ASCII 23383
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13836
14.9%
K 11024
11.9%
0 11024
11.9%
$ 11024
11.9%
s 5624
 
6.1%
n 5479
 
5.9%
4 4265
 
4.6%
- 3812
 
4.1%
e 2812
 
3.0%
L 2812
 
3.0%
Other values (12) 21164
22.8%
ValueCountFrequency (%)
3467
14.8%
K 2718
11.6%
0 2718
11.6%
$ 2718
11.6%
s 1498
 
6.4%
n 1418
 
6.1%
4 1086
 
4.6%
- 915
 
3.9%
e 749
 
3.2%
L 749
 
3.2%
Other values (12) 5347
22.9%

Card_Category
Categorical

 TrainTest
Distinct44
Distinct (%)< 0.1%0.2%
Missing00
Missing (%)0.0%0.0%
Memory size63.4 KiB16.0 KiB
Blue
7557 
Silver
 
436
Gold
 
93
Platinum
 
15
Blue
1879 
Silver
 
119
Gold
 
23
Platinum
 
5

Length

 TrainTest
Max length88
Median length44
Mean length4.11504754.1273445
Min length44

Characters and Unicode

 TrainTest
Total characters333368362
Distinct characters1616
Distinct categories22 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 TrainTest
Unique00 ?
Unique (%)0.0%0.0%

Sample

 TrainTest
1st rowBlueBlue
2nd rowBlueBlue
3rd rowGoldBlue
4th rowBlueBlue
5th rowBlueBlue

Common Values

ValueCountFrequency (%)
Blue 7557
93.3%
Silver 436
 
5.4%
Gold 93
 
1.1%
Platinum 15
 
0.2%
ValueCountFrequency (%)
Blue 1879
92.7%
Silver 119
 
5.9%
Gold 23
 
1.1%
Platinum 5
 
0.2%

Length

2023-04-12T11:59:47.374245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Train

2023-04-12T11:59:47.546794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:47.698818image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
blue 7557
93.3%
silver 436
 
5.4%
gold 93
 
1.1%
platinum 15
 
0.2%
ValueCountFrequency (%)
blue 1879
92.7%
silver 119
 
5.9%
gold 23
 
1.1%
platinum 5
 
0.2%

Most occurring characters

ValueCountFrequency (%)
l 8101
24.3%
e 7993
24.0%
u 7572
22.7%
B 7557
22.7%
i 451
 
1.4%
S 436
 
1.3%
v 436
 
1.3%
r 436
 
1.3%
G 93
 
0.3%
o 93
 
0.3%
Other values (6) 168
 
0.5%
ValueCountFrequency (%)
l 2026
24.2%
e 1998
23.9%
u 1884
22.5%
B 1879
22.5%
i 124
 
1.5%
S 119
 
1.4%
v 119
 
1.4%
r 119
 
1.4%
G 23
 
0.3%
o 23
 
0.3%
Other values (6) 48
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 25235
75.7%
Uppercase Letter 8101
 
24.3%
ValueCountFrequency (%)
Lowercase Letter 6336
75.8%
Uppercase Letter 2026
 
24.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 8101
32.1%
e 7993
31.7%
u 7572
30.0%
i 451
 
1.8%
v 436
 
1.7%
r 436
 
1.7%
o 93
 
0.4%
d 93
 
0.4%
a 15
 
0.1%
t 15
 
0.1%
Other values (2) 30
 
0.1%
ValueCountFrequency (%)
l 2026
32.0%
e 1998
31.5%
u 1884
29.7%
i 124
 
2.0%
v 119
 
1.9%
r 119
 
1.9%
o 23
 
0.4%
d 23
 
0.4%
a 5
 
0.1%
t 5
 
0.1%
Other values (2) 10
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
B 7557
93.3%
S 436
 
5.4%
G 93
 
1.1%
P 15
 
0.2%
ValueCountFrequency (%)
B 1879
92.7%
S 119
 
5.9%
G 23
 
1.1%
P 5
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 33336
100.0%
ValueCountFrequency (%)
Latin 8362
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 8101
24.3%
e 7993
24.0%
u 7572
22.7%
B 7557
22.7%
i 451
 
1.4%
S 436
 
1.3%
v 436
 
1.3%
r 436
 
1.3%
G 93
 
0.3%
o 93
 
0.3%
Other values (6) 168
 
0.5%
ValueCountFrequency (%)
l 2026
24.2%
e 1998
23.9%
u 1884
22.5%
B 1879
22.5%
i 124
 
1.5%
S 119
 
1.4%
v 119
 
1.4%
r 119
 
1.4%
G 23
 
0.3%
o 23
 
0.3%
Other values (6) 48
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33336
100.0%
ValueCountFrequency (%)
ASCII 8362
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 8101
24.3%
e 7993
24.0%
u 7572
22.7%
B 7557
22.7%
i 451
 
1.4%
S 436
 
1.3%
v 436
 
1.3%
r 436
 
1.3%
G 93
 
0.3%
o 93
 
0.3%
Other values (6) 168
 
0.5%
ValueCountFrequency (%)
l 2026
24.2%
e 1998
23.9%
u 1884
22.5%
B 1879
22.5%
i 124
 
1.5%
S 119
 
1.4%
v 119
 
1.4%
r 119
 
1.4%
G 23
 
0.3%
o 23
 
0.3%
Other values (6) 48
 
0.6%

Months_on_book
Real number (ℝ)

 TrainTest
Distinct4444
Distinct (%)0.5%2.2%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean35.9235935.94768
 TrainTest
Minimum1313
Maximum5656
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size63.4 KiB16.0 KiB
2023-04-12T11:59:47.891000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum1313
5-th percentile2222
Q13132
median3636
Q34040
95-th percentile5049
Maximum5656
Range4343
Interquartile range (IQR)98

Descriptive statistics

 TrainTest
Standard deviation8.02435887.8347896
Coefficient of variation (CV)0.223372970.2179498
Kurtosis0.357466350.58533273
Mean35.9235935.94768
Median Absolute Deviation (MAD)44
Skewness-0.10943003-0.093885469
Sum29101772830
Variance64.39033461.383928
MonotonicityNot monotonicNot monotonic
2023-04-12T11:59:48.401246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
36 1950
24.1%
39 276
 
3.4%
37 276
 
3.4%
38 274
 
3.4%
40 269
 
3.3%
34 267
 
3.3%
35 256
 
3.2%
31 255
 
3.1%
33 250
 
3.1%
41 243
 
3.0%
Other values (34) 3785
46.7%
ValueCountFrequency (%)
36 513
25.3%
34 86
 
4.2%
37 82
 
4.0%
38 73
 
3.6%
30 70
 
3.5%
39 65
 
3.2%
40 64
 
3.2%
31 63
 
3.1%
43 61
 
3.0%
42 61
 
3.0%
Other values (34) 888
43.8%
ValueCountFrequency (%)
13 57
0.7%
14 13
 
0.2%
15 28
 
0.3%
16 20
 
0.2%
17 31
 
0.4%
18 46
0.6%
19 54
0.7%
20 63
0.8%
21 71
0.9%
22 83
1.0%
ValueCountFrequency (%)
13 13
0.6%
14 3
 
0.1%
15 6
 
0.3%
16 9
0.4%
17 8
 
0.4%
18 12
0.6%
19 9
0.4%
20 11
0.5%
21 12
0.6%
22 22
1.1%
ValueCountFrequency (%)
13 13
0.2%
14 3
 
< 0.1%
15 6
 
0.1%
16 9
0.1%
17 8
 
0.1%
18 12
0.1%
19 9
0.1%
20 11
0.1%
21 12
0.1%
22 22
0.3%
ValueCountFrequency (%)
13 57
2.8%
14 13
 
0.6%
15 28
 
1.4%
16 20
 
1.0%
17 31
 
1.5%
18 46
2.3%
19 54
2.7%
20 63
3.1%
21 71
3.5%
22 83
4.1%

Total_Relationship_Count
Real number (ℝ)

 TrainTest
Distinct66
Distinct (%)0.1%0.3%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean3.81323293.8099704
 TrainTest
Minimum11
Maximum66
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size63.4 KiB16.0 KiB
2023-04-12T11:59:48.543786image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum11
5-th percentile11
Q133
median44
Q355
95-th percentile66
Maximum66
Range55
Interquartile range (IQR)22

Descriptive statistics

 TrainTest
Standard deviation1.55183771.5650249
Coefficient of variation (CV)0.406961150.41077089
Kurtosis-0.99950613-1.0317687
Mean3.81323293.8099704
Median Absolute Deviation (MAD)11
Skewness-0.16312656-0.15984669
Sum308917719
Variance2.40820022.449303
MonotonicityNot monotonicNot monotonic
2023-04-12T11:59:48.667834image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 1852
22.9%
4 1539
19.0%
5 1511
18.7%
6 1488
18.4%
2 985
12.2%
1 726
 
9.0%
ValueCountFrequency (%)
3 453
22.4%
5 380
18.8%
6 378
18.7%
4 373
18.4%
2 258
12.7%
1 184
9.1%
ValueCountFrequency (%)
1 726
 
9.0%
2 985
12.2%
3 1852
22.9%
4 1539
19.0%
5 1511
18.7%
6 1488
18.4%
ValueCountFrequency (%)
1 184
9.1%
2 258
12.7%
3 453
22.4%
4 373
18.4%
5 380
18.8%
6 378
18.7%
ValueCountFrequency (%)
1 184
2.3%
2 258
3.2%
3 453
5.6%
4 373
4.6%
5 380
4.7%
6 378
4.7%
ValueCountFrequency (%)
1 726
 
35.8%
2 985
48.6%
3 1852
91.4%
4 1539
76.0%
5 1511
74.6%
6 1488
73.4%

Months_Inactive_12_mon
Real number (ℝ)

 TrainTest
Distinct77
Distinct (%)0.1%0.3%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean2.34687082.3183613
 TrainTest
Minimum00
Maximum66
Zeros227
Zeros (%)0.3%0.3%
Negative00
Negative (%)0.0%0.0%
Memory size63.4 KiB16.0 KiB
2023-04-12T11:59:48.793361image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile11
Q122
median22
Q333
95-th percentile44
Maximum66
Range66
Interquartile range (IQR)11

Descriptive statistics

 TrainTest
Standard deviation1.01417690.99620439
Coefficient of variation (CV)0.432140060.42970196
Kurtosis1.12919680.96048225
Mean2.34687082.3183613
Median Absolute Deviation (MAD)11
Skewness0.644258490.58410244
Sum190124697
Variance1.02855470.99242319
MonotonicityNot monotonicNot monotonic
2023-04-12T11:59:48.910925image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 3094
38.2%
2 2611
32.2%
1 1780
22.0%
4 346
 
4.3%
5 144
 
1.8%
6 104
 
1.3%
0 22
 
0.3%
ValueCountFrequency (%)
3 752
37.1%
2 671
33.1%
1 453
22.4%
4 89
 
4.4%
5 34
 
1.7%
6 20
 
1.0%
0 7
 
0.3%
ValueCountFrequency (%)
0 22
 
0.3%
1 1780
22.0%
2 2611
32.2%
3 3094
38.2%
4 346
 
4.3%
5 144
 
1.8%
6 104
 
1.3%
ValueCountFrequency (%)
0 7
 
0.3%
1 453
22.4%
2 671
33.1%
3 752
37.1%
4 89
 
4.4%
5 34
 
1.7%
6 20
 
1.0%
ValueCountFrequency (%)
0 7
 
0.1%
1 453
5.6%
2 671
8.3%
3 752
9.3%
4 89
 
1.1%
5 34
 
0.4%
6 20
 
0.2%
ValueCountFrequency (%)
0 22
 
1.1%
1 1780
87.9%
2 2611
128.9%
3 3094
152.7%
4 346
 
17.1%
5 144
 
7.1%
6 104
 
5.1%

Contacts_Count_12_mon
Real number (ℝ)

 TrainTest
Distinct77
Distinct (%)0.1%0.3%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean2.45031482.4753208
 TrainTest
Minimum00
Maximum66
Zeros31287
Zeros (%)3.9%4.3%
Negative00
Negative (%)0.0%0.0%
Memory size63.4 KiB16.0 KiB
2023-04-12T11:59:49.042552image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile11
Q122
median23
Q333
95-th percentile44
Maximum66
Range66
Interquartile range (IQR)11

Descriptive statistics

 TrainTest
Standard deviation1.10068731.1281507
Coefficient of variation (CV)0.449202410.45575938
Kurtosis0.029614465-0.10290362
Mean2.45031482.4753208
Median Absolute Deviation (MAD)11
Skewness0.020659003-0.027423103
Sum198505015
Variance1.21151261.272724
MonotonicityNot monotonicNot monotonic
2023-04-12T11:59:49.161870image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 2716
33.5%
2 2596
32.0%
1 1207
14.9%
4 1092
13.5%
0 312
 
3.9%
5 133
 
1.6%
6 45
 
0.6%
ValueCountFrequency (%)
3 664
32.8%
2 631
31.1%
4 300
14.8%
1 292
14.4%
0 87
 
4.3%
5 43
 
2.1%
6 9
 
0.4%
ValueCountFrequency (%)
0 312
 
3.9%
1 1207
14.9%
2 2596
32.0%
3 2716
33.5%
4 1092
13.5%
5 133
 
1.6%
6 45
 
0.6%
ValueCountFrequency (%)
0 87
 
4.3%
1 292
14.4%
2 631
31.1%
3 664
32.8%
4 300
14.8%
5 43
 
2.1%
6 9
 
0.4%
ValueCountFrequency (%)
0 87
 
1.1%
1 292
3.6%
2 631
7.8%
3 664
8.2%
4 300
3.7%
5 43
 
0.5%
6 9
 
0.1%
ValueCountFrequency (%)
0 312
 
15.4%
1 1207
59.6%
2 2596
128.1%
3 2716
134.1%
4 1092
53.9%
5 133
 
6.6%
6 45
 
2.2%

Credit_Limit
Real number (ℝ)

 TrainTest
Distinct53251632
Distinct (%)65.7%80.6%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean8636.54818613.583
 TrainTest
Minimum1438.31438.3
Maximum3451634516
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size63.4 KiB16.0 KiB
2023-04-12T11:59:49.375138image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum1438.31438.3
5-th percentile1438.31479
Q125552571.25
median45494563.5
Q31112810707.5
95-th percentile3405834516
Maximum3451634516
Range33077.733077.7
Interquartile range (IQR)85738136.25

Descriptive statistics

 TrainTest
Standard deviation9086.41969100.4173
Coefficient of variation (CV)1.05208931.0565194
Kurtosis1.7776071.9418722
Mean8636.54818613.583
Median Absolute Deviation (MAD)25972589
Skewness1.65760931.7043103
Sum6996467617451119
Variance8256302082817594
MonotonicityNot monotonicNot monotonic
2023-04-12T11:59:49.627296image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1438.3 413
 
5.1%
34516 399
 
4.9%
9959 15
 
0.2%
15987 14
 
0.2%
23981 9
 
0.1%
6224 8
 
0.1%
2490 8
 
0.1%
3735 7
 
0.1%
7469 6
 
0.1%
1963 6
 
0.1%
Other values (5315) 7216
89.1%
ValueCountFrequency (%)
34516 109
 
5.4%
1438.3 94
 
4.6%
15987 4
 
0.2%
1734 4
 
0.2%
2214 4
 
0.2%
7469 4
 
0.2%
2609 4
 
0.2%
3735 4
 
0.2%
2687 3
 
0.1%
1621 3
 
0.1%
Other values (1622) 1793
88.5%
ValueCountFrequency (%)
1438.3 413
5.1%
1439 2
 
< 0.1%
1440 1
 
< 0.1%
1441 1
 
< 0.1%
1442 1
 
< 0.1%
1443 3
 
< 0.1%
1446 1
 
< 0.1%
1449 1
 
< 0.1%
1452 2
 
< 0.1%
1454 1
 
< 0.1%
ValueCountFrequency (%)
1438.3 94
4.6%
1441 1
 
< 0.1%
1449 1
 
< 0.1%
1451 2
 
0.1%
1460 1
 
< 0.1%
1472 1
 
< 0.1%
1476 1
 
< 0.1%
1479 2
 
0.1%
1486 1
 
< 0.1%
1488 1
 
< 0.1%
ValueCountFrequency (%)
1438.3 94
1.2%
1441 1
 
< 0.1%
1449 1
 
< 0.1%
1451 2
 
< 0.1%
1460 1
 
< 0.1%
1472 1
 
< 0.1%
1476 1
 
< 0.1%
1479 2
 
< 0.1%
1486 1
 
< 0.1%
1488 1
 
< 0.1%
ValueCountFrequency (%)
1438.3 413
20.4%
1439 2
 
0.1%
1440 1
 
< 0.1%
1441 1
 
< 0.1%
1442 1
 
< 0.1%
1443 3
 
0.1%
1446 1
 
< 0.1%
1449 1
 
< 0.1%
1452 2
 
0.1%
1454 1
 
< 0.1%

Total_Revolving_Bal
Real number (ℝ)

 TrainTest
Distinct18831013
Distinct (%)23.2%50.0%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean1160.38281172.5355
 TrainTest
Minimum00
Maximum25172517
Zeros1986484
Zeros (%)24.5%23.9%
Negative00
Negative (%)0.0%0.0%
Memory size63.4 KiB16.0 KiB
2023-04-12T11:59:49.892031image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q1326504.75
median12731290
Q317821793
95-th percentile25172489.75
Maximum25172517
Range25172517
Interquartile range (IQR)14561288.25

Descriptive statistics

 TrainTest
Standard deviation815.50429813.04525
Coefficient of variation (CV)0.702789030.69340777
Kurtosis-1.1491884-1.1323805
Mean1160.38281172.5355
Median Absolute Deviation (MAD)590594.5
Skewness-0.1444843-0.16633995
Sum94002612375557
Variance665047.25661042.57
MonotonicityNot monotonicNot monotonic
2023-04-12T11:59:50.146136image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1986
 
24.5%
2517 415
 
5.1%
1965 11
 
0.1%
1720 11
 
0.1%
1434 10
 
0.1%
1176 9
 
0.1%
1566 9
 
0.1%
1010 9
 
0.1%
1384 9
 
0.1%
1647 9
 
0.1%
Other values (1873) 5623
69.4%
ValueCountFrequency (%)
0 484
 
23.9%
2517 93
 
4.6%
1513 6
 
0.3%
1664 5
 
0.2%
2216 5
 
0.2%
1173 5
 
0.2%
845 4
 
0.2%
2312 4
 
0.2%
1396 4
 
0.2%
1580 4
 
0.2%
Other values (1003) 1412
69.7%
ValueCountFrequency (%)
0 1986
24.5%
134 1
 
< 0.1%
145 1
 
< 0.1%
154 1
 
< 0.1%
157 1
 
< 0.1%
159 1
 
< 0.1%
168 1
 
< 0.1%
170 1
 
< 0.1%
186 1
 
< 0.1%
193 2
 
< 0.1%
ValueCountFrequency (%)
0 484
23.9%
132 1
 
< 0.1%
159 1
 
< 0.1%
168 1
 
< 0.1%
191 1
 
< 0.1%
198 1
 
< 0.1%
214 1
 
< 0.1%
232 1
 
< 0.1%
234 1
 
< 0.1%
238 1
 
< 0.1%
ValueCountFrequency (%)
0 484
6.0%
132 1
 
< 0.1%
159 1
 
< 0.1%
168 1
 
< 0.1%
191 1
 
< 0.1%
198 1
 
< 0.1%
214 1
 
< 0.1%
232 1
 
< 0.1%
234 1
 
< 0.1%
238 1
 
< 0.1%
ValueCountFrequency (%)
0 1986
98.0%
134 1
 
< 0.1%
145 1
 
< 0.1%
154 1
 
< 0.1%
157 1
 
< 0.1%
159 1
 
< 0.1%
168 1
 
< 0.1%
170 1
 
< 0.1%
186 1
 
< 0.1%
193 2
 
0.1%

Avg_Open_To_Buy
Real number (ℝ)

 TrainTest
Distinct57571763
Distinct (%)71.1%87.0%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean7476.16537441.0475
 TrainTest
Minimum314
Maximum3451634516
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size63.4 KiB16.0 KiB
2023-04-12T11:59:50.401711image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum314
5-th percentile489448.8
Q113411255
median34953375.5
Q399429720
95-th percentile3209932443.5
Maximum3451634516
Range3451334502
Interquartile range (IQR)86018465

Descriptive statistics

 TrainTest
Standard deviation9080.27999134.3685
Coefficient of variation (CV)1.21456381.2275649
Kurtosis1.77261291.9081585
Mean7476.16537441.0475
Median Absolute Deviation (MAD)26742598.35
Skewness1.65400511.6932383
Sum6056441515075562
Variance8245148383436688
MonotonicityNot monotonicNot monotonic
2023-04-12T11:59:50.651476image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1438.3 261
 
3.2%
34516 81
 
1.0%
31999 21
 
0.3%
447 6
 
0.1%
787 6
 
0.1%
1129 6
 
0.1%
953 6
 
0.1%
990 6
 
0.1%
837 6
 
0.1%
933 6
 
0.1%
Other values (5747) 7696
95.0%
ValueCountFrequency (%)
1438.3 63
 
3.1%
34516 17
 
0.8%
31999 5
 
0.2%
913 4
 
0.2%
445 3
 
0.1%
817 3
 
0.1%
1045 3
 
0.1%
2652 3
 
0.1%
705 3
 
0.1%
684 3
 
0.1%
Other values (1753) 1919
94.7%
ValueCountFrequency (%)
3 1
< 0.1%
10 1
< 0.1%
14 1
< 0.1%
15 1
< 0.1%
24 1
< 0.1%
28 1
< 0.1%
36 1
< 0.1%
39 2
< 0.1%
41 2
< 0.1%
42 1
< 0.1%
ValueCountFrequency (%)
14 1
< 0.1%
29 1
< 0.1%
48 1
< 0.1%
65 1
< 0.1%
91 1
< 0.1%
106 1
< 0.1%
108 1
< 0.1%
111 1
< 0.1%
117 1
< 0.1%
130 1
< 0.1%
ValueCountFrequency (%)
14 1
< 0.1%
29 1
< 0.1%
48 1
< 0.1%
65 1
< 0.1%
91 1
< 0.1%
106 1
< 0.1%
108 1
< 0.1%
111 1
< 0.1%
117 1
< 0.1%
130 1
< 0.1%
ValueCountFrequency (%)
3 1
< 0.1%
10 1
< 0.1%
14 1
< 0.1%
15 1
< 0.1%
24 1
< 0.1%
28 1
< 0.1%
36 1
< 0.1%
39 2
0.1%
41 2
0.1%
42 1
< 0.1%

Total_Amt_Chng_Q4_Q1
Real number (ℝ)

 TrainTest
Distinct1089723
Distinct (%)13.4%35.7%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.760808790.7564694
 TrainTest
Minimum00
Maximum2.6753.397
Zeros41
Zeros (%)< 0.1%< 0.1%
Negative00
Negative (%)0.0%0.0%
Memory size63.4 KiB16.0 KiB
2023-04-12T11:59:50.919854image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile0.4660.45125
Q10.6320.625
median0.7380.7325
Q30.8590.85975
95-th percentile1.1061.09125
Maximum2.6753.397
Range2.6753.397
Interquartile range (IQR)0.2270.23475

Descriptive statistics

 TrainTest
Standard deviation0.216667810.2291003
Coefficient of variation (CV)0.284786150.30285468
Kurtosis6.621301620.802547
Mean0.760808790.7564694
Median Absolute Deviation (MAD)0.1130.1165
Skewness1.49132442.5480062
Sum6163.3121532.607
Variance0.0469449380.052486945
MonotonicityNot monotonicNot monotonic
2023-04-12T11:59:51.171734image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.767 28
 
0.3%
0.718 27
 
0.3%
0.76 27
 
0.3%
0.743 27
 
0.3%
0.725 26
 
0.3%
0.742 26
 
0.3%
0.69 26
 
0.3%
0.722 26
 
0.3%
0.791 26
 
0.3%
0.717 26
 
0.3%
Other values (1079) 7836
96.7%
ValueCountFrequency (%)
0.73 12
 
0.6%
0.735 11
 
0.5%
0.65 10
 
0.5%
0.691 10
 
0.5%
0.791 10
 
0.5%
0.693 9
 
0.4%
0.81 9
 
0.4%
0.68 9
 
0.4%
0.712 9
 
0.4%
0.644 8
 
0.4%
Other values (713) 1929
95.2%
ValueCountFrequency (%)
0 4
< 0.1%
0.01 1
 
< 0.1%
0.046 1
 
< 0.1%
0.061 2
< 0.1%
0.101 1
 
< 0.1%
0.12 1
 
< 0.1%
0.153 1
 
< 0.1%
0.163 1
 
< 0.1%
0.166 1
 
< 0.1%
0.175 1
 
< 0.1%
ValueCountFrequency (%)
0 1
< 0.1%
0.018 1
< 0.1%
0.072 1
< 0.1%
0.196 1
< 0.1%
0.202 2
0.1%
0.242 1
< 0.1%
0.262 1
< 0.1%
0.279 1
< 0.1%
0.298 1
< 0.1%
0.299 1
< 0.1%
ValueCountFrequency (%)
0 1
< 0.1%
0.018 1
< 0.1%
0.072 1
< 0.1%
0.196 1
< 0.1%
0.202 2
< 0.1%
0.242 1
< 0.1%
0.262 1
< 0.1%
0.279 1
< 0.1%
0.298 1
< 0.1%
0.299 1
< 0.1%
ValueCountFrequency (%)
0 4
0.2%
0.01 1
 
< 0.1%
0.046 1
 
< 0.1%
0.061 2
0.1%
0.101 1
 
< 0.1%
0.12 1
 
< 0.1%
0.153 1
 
< 0.1%
0.163 1
 
< 0.1%
0.166 1
 
< 0.1%
0.175 1
 
< 0.1%

Total_Trans_Amt
Real number (ℝ)

 TrainTest
Distinct44621702
Distinct (%)55.1%84.0%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean4402.98814408.4773
 TrainTest
Minimum510530
Maximum1848416920
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size63.4 KiB16.0 KiB
2023-04-12T11:59:51.443889image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum510530
5-th percentile12841282.75
Q121602147.25
median38973908
Q347394750.75
95-th percentile1421514203.5
Maximum1848416920
Range1797416390
Interquartile range (IQR)25792603.5

Descriptive statistics

 TrainTest
Standard deviation3401.70953379.5857
Coefficient of variation (CV)0.772591120.76661066
Kurtosis3.9049273.8612261
Mean4402.98814408.4773
Median Absolute Deviation (MAD)12981361
Skewness2.04795152.0141073
Sum356686078931575
Variance1157162811421599
MonotonicityNot monotonicNot monotonic
2023-04-12T11:59:51.699448image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4220 9
 
0.1%
4518 9
 
0.1%
4498 9
 
0.1%
4317 8
 
0.1%
4509 8
 
0.1%
4869 8
 
0.1%
4833 8
 
0.1%
4037 7
 
0.1%
1409 7
 
0.1%
4503 7
 
0.1%
Other values (4452) 8021
99.0%
ValueCountFrequency (%)
4697 4
 
0.2%
4253 4
 
0.2%
4444 4
 
0.2%
2229 4
 
0.2%
4531 4
 
0.2%
4598 4
 
0.2%
3016 4
 
0.2%
2461 3
 
0.1%
4143 3
 
0.1%
4552 3
 
0.1%
Other values (1692) 1989
98.2%
ValueCountFrequency (%)
510 1
< 0.1%
563 1
< 0.1%
569 1
< 0.1%
594 1
< 0.1%
596 1
< 0.1%
597 1
< 0.1%
602 1
< 0.1%
615 1
< 0.1%
644 1
< 0.1%
647 2
< 0.1%
ValueCountFrequency (%)
530 1
< 0.1%
643 1
< 0.1%
646 1
< 0.1%
704 1
< 0.1%
741 1
< 0.1%
777 1
< 0.1%
787 1
< 0.1%
791 1
< 0.1%
820 1
< 0.1%
821 1
< 0.1%
ValueCountFrequency (%)
530 1
< 0.1%
643 1
< 0.1%
646 1
< 0.1%
704 1
< 0.1%
741 1
< 0.1%
777 1
< 0.1%
787 1
< 0.1%
791 1
< 0.1%
820 1
< 0.1%
821 1
< 0.1%
ValueCountFrequency (%)
510 1
< 0.1%
563 1
< 0.1%
569 1
< 0.1%
594 1
< 0.1%
596 1
< 0.1%
597 1
< 0.1%
602 1
< 0.1%
615 1
< 0.1%
644 1
< 0.1%
647 2
0.1%

Total_Trans_Ct
Real number (ℝ)

 TrainTest
Distinct126119
Distinct (%)1.6%5.9%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean64.90778964.662389
 TrainTest
Minimum1010
Maximum139131
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size63.4 KiB16.0 KiB
2023-04-12T11:59:51.956635image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum1010
5-th percentile2828
Q14545
median6767
Q38180
95-th percentile105104.75
Maximum139131
Range129121
Interquartile range (IQR)3635

Descriptive statistics

 TrainTest
Standard deviation23.55637923.13914
Coefficient of variation (CV)0.362920680.35784542
Kurtosis-0.37133836-0.35031887
Mean64.90778964.662389
Median Absolute Deviation (MAD)1716
Skewness0.153617020.15304807
Sum525818131006
Variance554.90298535.41979
MonotonicityNot monotonicNot monotonic
2023-04-12T11:59:52.212053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77 168
 
2.1%
82 168
 
2.1%
81 164
 
2.0%
74 160
 
2.0%
75 160
 
2.0%
76 159
 
2.0%
69 159
 
2.0%
70 158
 
2.0%
73 157
 
1.9%
71 157
 
1.9%
Other values (116) 6491
80.1%
ValueCountFrequency (%)
71 46
 
2.3%
68 44
 
2.2%
81 44
 
2.2%
69 43
 
2.1%
75 43
 
2.1%
78 43
 
2.1%
72 41
 
2.0%
76 39
 
1.9%
79 38
 
1.9%
80 38
 
1.9%
Other values (109) 1607
79.3%
ValueCountFrequency (%)
10 1
 
< 0.1%
11 2
 
< 0.1%
12 4
 
< 0.1%
13 3
 
< 0.1%
14 8
 
0.1%
15 15
0.2%
16 9
0.1%
17 12
0.1%
18 21
0.3%
19 10
0.1%
ValueCountFrequency (%)
10 3
0.1%
13 2
 
0.1%
14 1
 
< 0.1%
15 1
 
< 0.1%
16 4
0.2%
17 1
 
< 0.1%
18 2
 
0.1%
19 1
 
< 0.1%
20 2
 
0.1%
21 7
0.3%
ValueCountFrequency (%)
10 3
< 0.1%
13 2
 
< 0.1%
14 1
 
< 0.1%
15 1
 
< 0.1%
16 4
< 0.1%
17 1
 
< 0.1%
18 2
 
< 0.1%
19 1
 
< 0.1%
20 2
 
< 0.1%
21 7
0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
11 2
 
0.1%
12 4
 
0.2%
13 3
 
0.1%
14 8
 
0.4%
15 15
0.7%
16 9
0.4%
17 12
0.6%
18 21
1.0%
19 10
0.5%

Total_Ct_Chng_Q4_Q1
Real number (ℝ)

 TrainTest
Distinct795558
Distinct (%)9.8%27.5%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.712176150.71240721
 TrainTest
Minimum00
Maximum3.7143.25
Zeros61
Zeros (%)0.1%< 0.1%
Negative00
Negative (%)0.0%0.0%
Memory size63.4 KiB16.0 KiB
2023-04-12T11:59:52.477133image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile0.3670.375
Q10.5830.579
median0.7020.702
Q30.8180.818
95-th percentile1.0691.068
Maximum3.7143.25
Range3.7143.25
Interquartile range (IQR)0.2350.239

Descriptive statistics

 TrainTest
Standard deviation0.239320790.23314183
Coefficient of variation (CV)0.336041570.32725922
Kurtosis16.55556911.833915
Mean0.712176150.71240721
Median Absolute Deviation (MAD)0.1170.12
Skewness2.12686481.791876
Sum5769.3391443.337
Variance0.0572744430.054355113
MonotonicityNot monotonicNot monotonic
2023-04-12T11:59:52.726350image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.667 141
 
1.7%
1 128
 
1.6%
0.75 128
 
1.6%
0.5 119
 
1.5%
0.6 95
 
1.2%
0.8 86
 
1.1%
0.714 77
 
1.0%
0.833 75
 
0.9%
0.778 57
 
0.7%
0.625 50
 
0.6%
Other values (785) 7145
88.2%
ValueCountFrequency (%)
0.5 42
 
2.1%
1 38
 
1.9%
0.667 30
 
1.5%
0.75 28
 
1.4%
0.6 18
 
0.9%
0.733 16
 
0.8%
0.8 15
 
0.7%
0.714 15
 
0.7%
0.66 13
 
0.6%
0.756 13
 
0.6%
Other values (548) 1798
88.7%
ValueCountFrequency (%)
0 6
0.1%
0.028 1
 
< 0.1%
0.029 1
 
< 0.1%
0.038 1
 
< 0.1%
0.053 1
 
< 0.1%
0.059 2
 
< 0.1%
0.062 1
 
< 0.1%
0.074 1
 
< 0.1%
0.077 1
 
< 0.1%
0.091 3
< 0.1%
ValueCountFrequency (%)
0 1
< 0.1%
0.077 2
0.1%
0.143 1
< 0.1%
0.15 1
< 0.1%
0.161 1
< 0.1%
0.162 1
< 0.1%
0.167 1
< 0.1%
0.171 1
< 0.1%
0.182 1
< 0.1%
0.188 1
< 0.1%
ValueCountFrequency (%)
0 1
< 0.1%
0.077 2
< 0.1%
0.143 1
< 0.1%
0.15 1
< 0.1%
0.161 1
< 0.1%
0.162 1
< 0.1%
0.167 1
< 0.1%
0.171 1
< 0.1%
0.182 1
< 0.1%
0.188 1
< 0.1%
ValueCountFrequency (%)
0 6
0.3%
0.028 1
 
< 0.1%
0.029 1
 
< 0.1%
0.038 1
 
< 0.1%
0.053 1
 
< 0.1%
0.059 2
 
0.1%
0.062 1
 
< 0.1%
0.074 1
 
< 0.1%
0.077 1
 
< 0.1%
0.091 3
0.1%

Avg_Utilization_Ratio
Real number (ℝ)

 TrainTest
Distinct943701
Distinct (%)11.6%34.6%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.273186640.28171866
 TrainTest
Minimum00
Maximum0.9990.992
Zeros1986484
Zeros (%)24.5%23.9%
Negative00
Negative (%)0.0%0.0%
Memory size63.4 KiB16.0 KiB
2023-04-12T11:59:53.002159image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum00
5-th percentile00
Q10.0220.026
median0.1740.184
Q30.4970.521
95-th percentile0.7890.8075
Maximum0.9990.992
Range0.9990.992
Interquartile range (IQR)0.4750.495

Descriptive statistics

 TrainTest
Standard deviation0.274594840.27999805
Coefficient of variation (CV)1.00515470.99389246
Kurtosis-0.77916141-0.85640731
Mean0.273186640.28171866
Median Absolute Deviation (MAD)0.1740.184
Skewness0.726094360.68569338
Sum2213.085570.762
Variance0.0754023240.078398907
MonotonicityNot monotonicNot monotonic
2023-04-12T11:59:53.257231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1986
 
24.5%
0.073 35
 
0.4%
0.057 26
 
0.3%
0.07 25
 
0.3%
0.048 25
 
0.3%
0.053 24
 
0.3%
0.06 24
 
0.3%
0.069 24
 
0.3%
0.061 23
 
0.3%
0.071 22
 
0.3%
Other values (933) 5887
72.7%
ValueCountFrequency (%)
0 484
 
23.9%
0.045 9
 
0.4%
0.073 9
 
0.4%
0.056 9
 
0.4%
0.041 8
 
0.4%
0.059 8
 
0.4%
0.057 7
 
0.3%
0.05 7
 
0.3%
0.048 7
 
0.3%
0.093 7
 
0.3%
Other values (691) 1471
72.6%
ValueCountFrequency (%)
0 1986
24.5%
0.004 1
 
< 0.1%
0.006 2
 
< 0.1%
0.007 1
 
< 0.1%
0.008 2
 
< 0.1%
0.009 1
 
< 0.1%
0.01 1
 
< 0.1%
0.011 1
 
< 0.1%
0.012 3
 
< 0.1%
0.013 2
 
< 0.1%
ValueCountFrequency (%)
0 484
23.9%
0.005 1
 
< 0.1%
0.006 1
 
< 0.1%
0.012 1
 
< 0.1%
0.014 1
 
< 0.1%
0.016 5
 
0.2%
0.017 1
 
< 0.1%
0.019 4
 
0.2%
0.02 1
 
< 0.1%
0.021 4
 
0.2%
ValueCountFrequency (%)
0 484
6.0%
0.005 1
 
< 0.1%
0.006 1
 
< 0.1%
0.012 1
 
< 0.1%
0.014 1
 
< 0.1%
0.016 5
 
0.1%
0.017 1
 
< 0.1%
0.019 4
 
< 0.1%
0.02 1
 
< 0.1%
0.021 4
 
< 0.1%
ValueCountFrequency (%)
0 1986
98.0%
0.004 1
 
< 0.1%
0.006 2
 
0.1%
0.007 1
 
< 0.1%
0.008 2
 
0.1%
0.009 1
 
< 0.1%
0.01 1
 
< 0.1%
0.011 1
 
< 0.1%
0.012 3
 
0.1%
0.013 2
 
0.1%

Attrition_Flag
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.4 KiB
1
6801 
0
1300 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8101
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 6801
84.0%
0 1300
 
16.0%

Length

2023-04-12T11:59:53.434377image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 6801
84.0%
0 1300
 
16.0%

Most occurring characters

ValueCountFrequency (%)
1 6801
84.0%
0 1300
 
16.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8101
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6801
84.0%
0 1300
 
16.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8101
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6801
84.0%
0 1300
 
16.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8101
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6801
84.0%
0 1300
 
16.0%

Interactions

Train

2023-04-12T11:59:02.873588image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test


Interaction plot not present for dataset

Train

2023-04-12T11:58:26.838988image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test


Interaction plot not present for dataset

Train

2023-04-12T11:58:29.138981image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test


Interaction plot not present for dataset

Train

2023-04-12T11:58:31.332852image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test


Interaction plot not present for dataset

Train

2023-04-12T11:58:33.377315image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test


Interaction plot not present for dataset

Train

2023-04-12T11:58:35.676725image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test


Interaction plot not present for dataset

Train

2023-04-12T11:58:37.682546image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test


Interaction plot not present for dataset

Train

2023-04-12T11:58:39.791003image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test


Interaction plot not present for dataset

Train

2023-04-12T11:58:42.059624image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test


Interaction plot not present for dataset

Train

2023-04-12T11:58:44.537221image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test


Interaction plot not present for dataset

Train

2023-04-12T11:58:47.073851image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test


Interaction plot not present for dataset

Train

2023-04-12T11:58:49.732012image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test


Interaction plot not present for dataset

Train

2023-04-12T11:58:52.267268image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test


Interaction plot not present for dataset

Train

2023-04-12T11:58:54.746542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test


Interaction plot not present for dataset

Train

2023-04-12T11:58:57.426074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test


Interaction plot not present for dataset

Train

2023-04-12T11:59:00.334687image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test


Interaction plot not present for dataset

Train

2023-04-12T11:59:03.041453image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:40.613930image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:26.993172image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test


Interaction plot not present for dataset

Train

2023-04-12T11:58:29.289053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:07.716472image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:31.470430image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:10.016085image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:33.746771image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:12.193225image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:35.816929image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:14.392086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:37.824741image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:16.525361image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:39.936178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:18.767668image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:42.225489image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:21.313073image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:44.717409image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:23.860596image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:47.239067image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:26.234517image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:49.901681image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:28.478549image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:52.434303image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:30.812731image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:54.977599image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:33.544533image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:57.684280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:35.864346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:59:00.511282image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:38.309495image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:59:03.190603image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:40.761004image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:27.139794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test


Interaction plot not present for dataset

Train

2023-04-12T11:58:29.418569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:07.888620image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:31.593578image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:10.160260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:33.877389image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:12.343259image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:35.943419image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:14.531388image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:37.955361image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:16.672505image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:40.065778image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:18.915305image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:42.380080image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:21.462251image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:44.875000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:24.020163image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:47.389645image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:26.381557image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:50.060826image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:28.634212image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:52.583835image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:30.965003image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:55.160869image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:33.700479image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:57.897444image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:36.012974image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:59:00.682395image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:38.460451image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:59:03.332375image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:40.899564image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:27.272469image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test


Interaction plot not present for dataset

Train

2023-04-12T11:58:29.552150image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:08.044878image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:31.713227image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:10.290885image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:33.996851image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:12.479470image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:36.057016image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:14.661565image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:38.079599image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:16.809029image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:40.192415image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:19.058524image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:42.524235image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:21.600874image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:45.023231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:24.188271image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:47.532696image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:26.524182image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:50.206930image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:28.776392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:52.724898image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:31.108629image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:55.327972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:33.845839image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:58.141748image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:36.154669image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:59:00.832580image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:38.600980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:59:03.480993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:41.048156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:27.409582image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test


Interaction plot not present for dataset

Train

2023-04-12T11:58:29.683357image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:08.193500image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:31.835587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:10.430094image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:34.121147image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:12.619124image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:36.176707image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:14.796193image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:38.204787image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:16.949814image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:40.318568image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:19.204601image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:42.671867image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:21.747647image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:45.175855image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:24.350054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:47.679323image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:26.666363image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:50.358556image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:28.928028image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:52.871557image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:31.262778image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:55.489513image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:33.993994image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:58.335782image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:36.303293image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:59:00.987128image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:38.750045image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:59:03.615280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:41.182349image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:27.536168image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test


Interaction plot not present for dataset

Train

2023-04-12T11:58:29.805974image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:08.330717image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:31.948010image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:10.555695image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:34.236383image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:12.750218image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:36.284314image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:14.917762image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:38.322427image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:17.083405image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:40.445374image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:19.341778image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:42.810720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:21.880837image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:45.321922image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:24.494234image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:47.820562image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:26.798925image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:50.497253image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:29.068161image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:53.005784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:31.399963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:55.636707image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:34.138811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:58.529875image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:36.437886image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:59:01.130234image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:38.887688image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:59:03.768394image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:41.328945image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:27.682291image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test


Interaction plot not present for dataset

Train

2023-04-12T11:58:29.943533image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:08.478347image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:32.073670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:10.698318image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:34.362619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:12.893825image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:36.406261image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:15.068852image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:38.449025image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:17.231960image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:40.579899image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:19.493080image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:42.961904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:22.029494image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:45.480459image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:24.648985image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:48.236115image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:26.948690image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:50.654464image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:29.220785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:53.156362image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:31.559150image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:55.798963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:34.292985image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:58.714996image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:36.583000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:59:01.281851image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:39.057785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:59:03.922617image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:41.480936image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:27.833543image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test


Interaction plot not present for dataset

Train

2023-04-12T11:58:30.084633image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:08.628276image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:32.203772image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:10.841470image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:34.493874image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:13.041916image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:36.536767image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:15.213482image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:38.577715image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:17.386556image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:40.712370image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:19.647058image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:43.119154image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:22.182188image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:45.640494image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:24.802566image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:48.370741image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:27.097914image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:50.818724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:29.381933image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:53.310580image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:31.800325image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:55.960104image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:34.455124image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:58.880441image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:36.731631image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:59:01.439240image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:39.209297image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:59:04.078102image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:41.633462image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:27.976161image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test


Interaction plot not present for dataset

Train

2023-04-12T11:58:30.220222image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:08.779789image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:32.331352image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:10.987106image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:34.622370image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:13.189058image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:36.663389image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:15.358622image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:38.727031image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:17.538735image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:40.841942image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:19.800246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:43.271783image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:22.434294image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:45.799694image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:24.958376image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:48.511967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:27.252480image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:50.980817image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:29.538530image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:53.462208image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:32.007958image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:56.123374image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:34.607726image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:59.041695image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:37.131153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:59:01.593874image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:39.366329image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:59:04.487493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:41.793519image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:28.126260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test


Interaction plot not present for dataset

Train

2023-04-12T11:58:30.362565image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:08.945066image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:32.463975image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:11.139866image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:34.756997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:13.345093image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:36.792595image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:15.513153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:38.863651image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:17.698265image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:40.988606image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:19.962502image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:43.435542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:22.635529image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:45.963322image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:25.121906image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:48.664493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:27.413075image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:51.146045image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:29.699233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:53.623923image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:32.211216image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:56.288000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:34.770980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:59.204147image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:37.275352image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:59:01.759027image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:39.524585image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:59:04.623622image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:41.936744image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:28.259926image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test


Interaction plot not present for dataset

Train

2023-04-12T11:58:30.500102image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:09.092669image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:32.595045image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:11.278976image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:34.882559image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:13.485293image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:36.914238image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:15.654771image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:38.988319image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:17.840347image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:41.115828image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:20.111156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:43.585114image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:22.810126image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:46.118384image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:25.275937image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:48.807725image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:27.557003image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:51.303280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:29.851016image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:53.781549image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:32.379445image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:56.447037image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:34.919196image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:59.356234image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:37.413938image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:59:01.917821image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:39.674766image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:59:04.776246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:42.098916image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:28.405121image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test


Interaction plot not present for dataset

Train

2023-04-12T11:58:30.640661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:09.247917image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:32.731195image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:11.436230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:35.019703image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:13.641977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:37.044863image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:15.803388image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:39.126497image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:18.005001image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:41.259350image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:20.268281image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:43.748339image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:23.003890image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:46.280989image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:25.434587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:48.963253image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:27.718168image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:51.466430image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:30.011649image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:53.944811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:32.653131image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:56.614327image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:35.081599image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:59.523322image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:37.567158image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:59:02.085055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:39.834388image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:59:04.921506image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:42.260100image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:28.539354image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test


Interaction plot not present for dataset

Train

2023-04-12T11:58:30.778216image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:09.405541image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:32.855781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:11.591855image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:35.145309image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:13.796607image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:37.166446image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:15.953611image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:39.252122image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:18.167202image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:41.406057image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:20.433396image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:43.900023image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:23.208982image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:46.432162image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:25.600713image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:49.112529image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:27.880204image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:51.622498image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:30.181837image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:54.093396image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:32.864353image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:56.767008image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:35.243249image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:59.668893image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:37.722783image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:59:02.233412image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:39.996787image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:59:05.077132image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:42.423726image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:28.692498image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test


Interaction plot not present for dataset

Train

2023-04-12T11:58:30.924456image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:09.559757image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:32.990586image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:11.746080image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:35.283937image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:13.953751image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:37.300787image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:16.103164image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:39.396233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:18.326834image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:41.568684image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:20.593075image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:44.070691image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:23.392091image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:46.594800image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:25.765963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:49.267149image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:28.034826image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:51.784106image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:30.347647image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:54.253467image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:33.052383image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:56.939039image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:35.401382image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:59.839106image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:37.877044image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:59:02.397602image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:40.161998image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:59:05.231240image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:42.570776image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:28.842175image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test


Interaction plot not present for dataset

Train

2023-04-12T11:58:31.060044image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:09.707392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:33.114761image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:11.882672image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:35.411549image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:14.095846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:37.426373image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:16.241226image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:39.525749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:18.473879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:41.738767image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:21.006709image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:44.226822image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:23.547309image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:46.750946image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:25.919595image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:49.420211image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:28.181916image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:51.948398image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:30.501281image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:54.402125image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:33.219769image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:57.100708image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:35.550429image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:59.991766image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:38.017660image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:59:02.551866image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:40.309650image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:59:05.386859image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:42.722589image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:28.996397image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test


Interaction plot not present for dataset

Train

2023-04-12T11:58:31.199232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:09.864529image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:33.253663image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:12.047084image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:35.548143image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:14.245910image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:37.559894image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:16.386841image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:39.663376image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:18.621537image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:41.906991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:21.166914image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:44.391509image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:23.706792image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:46.920222image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:26.075975image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:49.581759image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:28.334414image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:52.112626image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:30.659103image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:54.565413image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:33.388897image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:58:57.267919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:35.714190image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:59:00.172579image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:38.165871image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

2023-04-12T11:59:02.718958image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:40.464687image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

Train

2023-04-12T11:59:53.587383image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Test

2023-04-12T11:59:53.941267image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Train

train_idxCLIENTNUMCustomer_AgeDependent_countMonths_on_bookTotal_Relationship_CountMonths_Inactive_12_monContacts_Count_12_monCredit_LimitTotal_Revolving_BalAvg_Open_To_BuyTotal_Amt_Chng_Q4_Q1Total_Trans_AmtTotal_Trans_CtTotal_Ct_Chng_Q4_Q1Avg_Utilization_RatioGenderEducation_LevelMarital_StatusIncome_CategoryCard_CategoryAttrition_Flag
train_idx1.0000.0020.002-0.0040.015-0.0090.0030.001-0.006-0.005-0.0030.0050.0140.0180.0090.0020.0070.0070.0000.0000.0000.000
CLIENTNUM0.0021.0000.017-0.0200.1110.017-0.0090.0100.015-0.0010.0140.028-0.0020.0050.0090.0030.0000.0150.0000.0000.0000.045
Customer_Age0.0020.0171.000-0.1390.773-0.0120.039-0.000-0.0030.011-0.006-0.077-0.036-0.057-0.0320.0100.0000.0240.0910.0720.0240.037
Dependent_count-0.004-0.020-0.1391.000-0.115-0.0310.001-0.0480.0560.0030.056-0.0280.0510.0430.001-0.0320.0090.0140.0400.0440.0130.014
Months_on_book0.0150.1110.773-0.1151.000-0.0110.0580.0030.0030.0100.004-0.063-0.030-0.045-0.032-0.0020.0000.0000.0500.0480.0030.015
Total_Relationship_Count-0.0090.017-0.012-0.031-0.0111.000-0.0150.060-0.0590.011-0.0710.028-0.271-0.2200.0190.0650.0200.0000.0230.0130.0680.164
Months_Inactive_12_mon0.003-0.0090.0390.0010.058-0.0151.0000.031-0.033-0.041-0.022-0.026-0.028-0.046-0.051-0.0210.0200.0000.0080.0180.0000.203
Contacts_Count_12_mon0.0010.010-0.000-0.0480.0030.0600.0311.0000.024-0.0500.038-0.019-0.176-0.177-0.096-0.0680.0630.0000.0000.0230.0000.246
Credit_Limit-0.0060.015-0.0030.0560.003-0.059-0.0330.0241.0000.1420.9320.0150.0240.033-0.016-0.4110.4430.0000.0300.2780.3350.038
Total_Revolving_Bal-0.005-0.0010.0110.0030.0100.011-0.041-0.0500.1421.000-0.1430.0330.0240.0500.0820.7050.0330.0160.0100.0210.0230.403
Avg_Open_To_Buy-0.0030.014-0.0060.0560.004-0.071-0.0220.0380.932-0.1431.0000.0020.0150.017-0.045-0.6810.4450.0000.0300.2780.3360.024
Total_Amt_Chng_Q4_Q10.0050.028-0.077-0.028-0.0630.028-0.026-0.0190.0150.0330.0021.0000.1280.0770.2910.0330.0650.0170.0560.0300.0120.229
Total_Trans_Amt0.014-0.002-0.0360.051-0.030-0.271-0.028-0.1760.0240.0240.0150.1281.0000.8800.2280.0300.2500.0070.1050.0950.1590.325
Total_Trans_Ct0.0180.005-0.0570.043-0.045-0.220-0.046-0.1770.0330.0500.0170.0770.8801.0000.2350.0500.1660.0010.1000.0600.1100.464
Total_Ct_Chng_Q4_Q10.0090.009-0.0320.001-0.0320.019-0.051-0.096-0.0160.082-0.0450.2910.2280.2351.0000.0980.0480.0050.0310.0240.0000.317
Avg_Utilization_Ratio0.0020.0030.010-0.032-0.0020.065-0.021-0.068-0.4110.705-0.6810.0330.0300.0500.0981.0000.2790.0000.0270.1650.1450.242
Gender0.0070.0000.0000.0090.0000.0200.0200.0630.4430.0330.4450.0650.2500.1660.0480.2791.0000.0180.0170.8410.0810.046
Education_Level0.0070.0150.0240.0140.0000.0000.0000.0000.0000.0160.0000.0170.0070.0010.0050.0000.0181.0000.0160.0160.0200.038
Marital_Status0.0000.0000.0910.0400.0500.0230.0080.0000.0300.0100.0300.0560.1050.1000.0310.0270.0170.0161.0000.0150.0250.000
Income_Category0.0000.0000.0720.0440.0480.0130.0180.0230.2780.0210.2780.0300.0950.0600.0240.1650.8410.0160.0151.0000.0530.029
Card_Category0.0000.0000.0240.0130.0030.0680.0000.0000.3350.0230.3360.0120.1590.1100.0000.1450.0810.0200.0250.0531.0000.016
Attrition_Flag0.0000.0450.0370.0140.0150.1640.2030.2460.0380.4030.0240.2290.3250.4640.3170.2420.0460.0380.0000.0290.0161.000

Test

CLIENTNUMCustomer_AgeDependent_countMonths_on_bookTotal_Relationship_CountMonths_Inactive_12_monContacts_Count_12_monCredit_LimitTotal_Revolving_BalAvg_Open_To_BuyTotal_Amt_Chng_Q4_Q1Total_Trans_AmtTotal_Trans_CtTotal_Ct_Chng_Q4_Q1Avg_Utilization_RatioGenderEducation_LevelMarital_StatusIncome_CategoryCard_Category
CLIENTNUM1.0000.0190.0580.1060.001-0.0060.0170.0110.0180.0020.0070.0010.0110.0440.0210.0390.0080.0000.0000.000
Customer_Age0.0191.000-0.1630.752-0.0240.068-0.0720.0230.0230.012-0.045-0.049-0.040-0.0730.0110.0000.0000.0460.0800.000
Dependent_count0.058-0.1631.000-0.116-0.054-0.048-0.0190.028-0.0290.048-0.0180.0850.0940.043-0.0450.0000.0000.0180.0370.006
Months_on_book0.1060.752-0.1161.000-0.0260.056-0.0530.023-0.0090.025-0.017-0.028-0.013-0.042-0.0110.0360.0120.0000.0490.020
Total_Relationship_Count0.001-0.024-0.054-0.0261.0000.0250.066-0.0600.014-0.0690.016-0.311-0.2560.0450.0680.0000.0000.0280.0110.065
Months_Inactive_12_mon-0.0060.068-0.0480.0560.0251.0000.027-0.004-0.0490.0090.007-0.047-0.073-0.030-0.0480.0000.0230.0000.0290.000
Contacts_Count_12_mon0.017-0.072-0.019-0.0530.0660.0271.0000.019-0.0230.015-0.029-0.134-0.135-0.083-0.0230.0660.0000.0000.0120.000
Credit_Limit0.0110.0230.0280.023-0.060-0.0040.0191.0000.0890.9310.0480.0470.0380.007-0.4390.4240.0000.0440.2820.349
Total_Revolving_Bal0.0180.023-0.029-0.0090.014-0.049-0.0230.0891.000-0.1990.050-0.0090.0010.0640.7220.0000.0000.0000.0320.000
Avg_Open_To_Buy0.0020.0120.0480.025-0.0690.0090.0150.931-0.1991.0000.0260.0510.039-0.020-0.7040.4200.0000.0300.2800.352
Total_Amt_Chng_Q4_Q10.007-0.045-0.018-0.0170.0160.007-0.0290.0480.0500.0261.0000.1610.1190.3460.0310.0300.0000.0500.0000.000
Total_Trans_Amt0.001-0.0490.085-0.028-0.311-0.047-0.1340.047-0.0090.0510.1611.0000.8800.204-0.0210.2260.0120.1060.0780.167
Total_Trans_Ct0.011-0.0400.094-0.013-0.256-0.073-0.1350.0380.0010.0390.1190.8801.0000.2290.0010.1620.0000.0780.0610.144
Total_Ct_Chng_Q4_Q10.044-0.0730.043-0.0420.045-0.030-0.0830.0070.064-0.0200.3460.2040.2291.0000.0800.0640.0290.0190.0000.000
Avg_Utilization_Ratio0.0210.011-0.045-0.0110.068-0.048-0.023-0.4390.722-0.7040.031-0.0210.0010.0801.0000.2770.0000.0050.1620.151
Gender0.0390.0000.0000.0360.0000.0000.0660.4240.0000.4200.0300.2260.1620.0640.2771.0000.0000.0000.8340.098
Education_Level0.0080.0000.0000.0120.0000.0230.0000.0000.0000.0000.0000.0120.0000.0290.0000.0001.0000.0030.0000.000
Marital_Status0.0000.0460.0180.0000.0280.0000.0000.0440.0000.0300.0500.1060.0780.0190.0050.0000.0031.0000.0000.024
Income_Category0.0000.0800.0370.0490.0110.0290.0120.2820.0320.2800.0000.0780.0610.0000.1620.8340.0000.0001.0000.075
Card_Category0.0000.0000.0060.0200.0650.0000.0000.3490.0000.3520.0000.1670.1440.0000.1510.0980.0000.0240.0751.000

Missing values

Train

2023-04-12T11:59:05.637587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.

Test

2023-04-12T11:59:42.965856image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.

Train

2023-04-12T11:59:06.136367image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Test

2023-04-12T11:59:43.400571image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Train

train_idxCLIENTNUMCustomer_AgeGenderDependent_countEducation_LevelMarital_StatusIncome_CategoryCard_CategoryMonths_on_bookTotal_Relationship_CountMonths_Inactive_12_monContacts_Count_12_monCredit_LimitTotal_Revolving_BalAvg_Open_To_BuyTotal_Amt_Chng_Q4_Q1Total_Trans_AmtTotal_Trans_CtTotal_Ct_Chng_Q4_Q1Avg_Utilization_RatioAttrition_Flag
0071307138354F1UnknownSingleUnknownBlue361333723.017281995.00.5958554990.6780.4641
1171424633358F4High SchoolMarriedUnknownBlue481435396.018033593.00.4932107390.3930.3340
2271820678345F4UnknownSingleLess than $40KGold3661315987.0164814339.00.7321436361.2500.1031
3372109698334F2GraduateSingleLess than $40KBlue364343625.025171108.01.1582616461.3000.6941
4472002868349F2High SchoolMarried$40K - $60KBlue395342720.01926794.00.6023806610.7940.7081
5577894223360F0DoctorateMarriedLess than $40KBlue455241438.3648790.30.4771267271.0770.4511
6670868290843F4UnknownSingleUnknownBlue282212838.01934904.00.8738644870.5540.6811
7772067045852F2UnknownSingle$40K - $60KBlue453133476.015601916.00.8943496580.8710.4491
8871995240830M0GraduateMarriedLess than $40KBlue363322550.01623927.00.6501870510.2750.6361
9970841275833F3GraduateSingleLess than $40KBlue365231457.001457.00.6772200450.3640.0000

Test

CLIENTNUMCustomer_AgeGenderDependent_countEducation_LevelMarital_StatusIncome_CategoryCard_CategoryMonths_on_bookTotal_Relationship_CountMonths_Inactive_12_monContacts_Count_12_monCredit_LimitTotal_Revolving_BalAvg_Open_To_BuyTotal_Amt_Chng_Q4_Q1Total_Trans_AmtTotal_Trans_CtTotal_Ct_Chng_Q4_Q1Avg_Utilization_Ratio
071945508348F3UneducatedSingleLess than $40KBlue394342991.015081483.00.7033734640.8820.504
177350330859M1UneducatedSingleLess than $40KBlue535542192.01569623.00.7064010790.7170.716
271545240837F2GraduateDivorcedLess than $40KBlue364331734.0987747.00.8794727670.9140.569
371126403347M3DoctorateDivorced$40K - $60KBlue364234786.015163270.00.9404973740.8500.317
471894350842M3UnknownSingle$80K - $120KBlue333323714.021701544.00.5241454350.5220.584
577824735865M1GraduateSingleLess than $40KBlue565327636.007636.00.8013880670.8110.000
671043115852F3UnknownSingleUnknownBlue3631210273.016578616.00.7103778700.5910.161
771525238358F2High SchoolDivorcedUnknownGold3654334516.0186432652.00.6643595520.7330.054
871718918335M1DoctorateSingle$40K - $60KBlue2463210467.019618506.00.7132665650.6670.187
971205093348M4CollegeMarried$80K - $120KBlue3252325190.0025190.00.4671533410.3230.000

Train

train_idxCLIENTNUMCustomer_AgeGenderDependent_countEducation_LevelMarital_StatusIncome_CategoryCard_CategoryMonths_on_bookTotal_Relationship_CountMonths_Inactive_12_monContacts_Count_12_monCredit_LimitTotal_Revolving_BalAvg_Open_To_BuyTotal_Amt_Chng_Q4_Q1Total_Trans_AmtTotal_Trans_CtTotal_Ct_Chng_Q4_Q1Avg_Utilization_RatioAttrition_Flag
8091809171482540845F3High SchoolSingle$40K - $60KBlue362332853.02517336.00.5954971650.7570.8821
8092809281246595853F3Post-GraduateDivorcedUnknownBlue4852212286.099711289.00.7264960830.7290.0811
8093809370927458340M2High SchoolMarried$120K +Blue2753112248.0132310925.00.8824806910.8960.1081
8094809470821775863M2GraduateMarried$60K - $80KBlue4952314035.0206111974.02.2711606301.5000.1471
8095809571814835850F3High SchoolMarriedLess than $40KBlue362331572.001572.00.7402447410.5770.0000
8096809676905303344F1GraduateSingle$40K - $60KBlue383254142.025171625.00.8092104440.8330.6080
8097809771440615853F3High SchoolDivorcedUnknownBlue364367939.007939.00.5512269420.3120.0000
8098809871414013342F4GraduateUnknownLess than $40KBlue323122314.01547767.00.8044678741.0000.6691
8099809972024498340M3UnknownSingle$40K - $60KBlue284113563.017071856.00.5061482420.3120.4791
8100810082712388353M4High SchoolSingle$60K - $80KBlue495123858.003858.00.6704472920.6140.0001

Test

CLIENTNUMCustomer_AgeGenderDependent_countEducation_LevelMarital_StatusIncome_CategoryCard_CategoryMonths_on_bookTotal_Relationship_CountMonths_Inactive_12_monContacts_Count_12_monCredit_LimitTotal_Revolving_BalAvg_Open_To_BuyTotal_Amt_Chng_Q4_Q1Total_Trans_AmtTotal_Trans_CtTotal_Ct_Chng_Q4_Q1Avg_Utilization_Ratio
201677129223348F1UnknownSingleLess than $40KBlue361123918.08133105.01.149158671031.0600.208
201771571633362F0High SchoolSingleUnknownBlue5062218819.0018819.00.8944273870.9770.000
201878167598359F0GraduateMarriedLess than $40KBlue534513054.02517537.01.0614706840.6470.824
201970825345859F1UnknownSingleUnknownBlue484235528.018233705.00.7314857810.7230.330
202071866503344M5DoctorateSingle$60K - $80KSilver3623434140.0034140.00.8778177760.5200.000
202181477603334M2GraduateSingle$80K - $120KBlue2931313395.0167811717.01.0062650690.8650.125
202272044440835F1CollegeSingleLess than $40KBlue252242231.01791440.00.8202576420.7500.803
202372050350844F1UneducatedDivorcedLess than $40KBlue371235594.012354359.00.5495220750.8290.221
202472121728327M0GraduateSingle$120K +Blue176228713.013547359.00.5582094360.3330.155
202577092090839F0UnknownSingle$40K - $60KSilver2611122054.0114620908.00.8428055820.6730.052

Duplicate rows

Train

train_idxCLIENTNUMCustomer_AgeGenderDependent_countEducation_LevelMarital_StatusIncome_CategoryCard_CategoryMonths_on_bookTotal_Relationship_CountMonths_Inactive_12_monContacts_Count_12_monCredit_LimitTotal_Revolving_BalAvg_Open_To_BuyTotal_Amt_Chng_Q4_Q1Total_Trans_AmtTotal_Trans_CtTotal_Ct_Chng_Q4_Q1Avg_Utilization_RatioAttrition_Flag# duplicates
Dataset does not contain duplicate rows.

Test

CLIENTNUMCustomer_AgeGenderDependent_countEducation_LevelMarital_StatusIncome_CategoryCard_CategoryMonths_on_bookTotal_Relationship_CountMonths_Inactive_12_monContacts_Count_12_monCredit_LimitTotal_Revolving_BalAvg_Open_To_BuyTotal_Amt_Chng_Q4_Q1Total_Trans_AmtTotal_Trans_CtTotal_Ct_Chng_Q4_Q1Avg_Utilization_Ratio# duplicates
Dataset does not contain duplicate rows.